The present invention relates to analyzing chemical reactions and, more particularly, but not exclusively to systems and methods for remote classification of chemical reaction assays.
Traditionally, the classification of the assays is based on manual examination by an expert in the field. The expert manually examines hundreds or thousands of samples, say thousands of graphs derived from Quantitative-Fluorescent Polymerase Chain Reaction (QF-PCR) based assays.
The expert manually detects certain features in the samples, and classifies each sample into one of two or more groups of chemical reactions (say as negative or positive, with respect to presence of a certain genetic mutation).
Some currently used methods provide for semi-automatic classification of chemical reaction assays.
For example, PCT Patent Application No.: PCT/IB2006/051025, filed on Apr. 4, 2006, to Tichopad et al., entitled “Assessment of Reaction Kinetics Compatibility between Polymerase Chain Reactions”, describes the usage of a large training set, to statistically compare properties of chemical assays.
Similarly, Wold et al, describe in a 1977 article, entitled “SIMCA: A method for analyzing chemical data in terms of similarity and analogy”, in Kowalski, B. R., ed., Chemometrics Theory and Application, American Chemical Society Symposium Series 52, Wash., D.C., American Chemical Society, pp. 243-282, a similarity analysis method. The Wold method requires availability of a large amount of test sample data, with a multitude of attributes and class memberships.
However, the above described methods still rely on a training set built manually, by the expert. In order to build the training set, the expert has to manually examine hundreds or thousands of samples, and classify each sample into one of two or more groups of chemical reactions.
Some currently used methods are based on automatic classification of samples. For example, some of the currently used methods use SVM (Support Vector Machine), to identify patterns in biological systems.
Support Vector Machine (SVM) is a set of related supervised learning methods that analyze data and recognize patterns. Supervised learning methods are widely used for classification and regression analysis.
Intuitively, an SVM built model is a representation of the samples as points in a space, mapped so that the examples of the separate categories are divided by a clear gap.
Typically, a preliminary step in the classification of chemical reaction assays involves feature extraction from a large number of test assays.
In one example, the feature extraction includes, among others, extraction of parameters from a QF-PCR chemical reaction graph of each of the test assays, say extraction of coordinate values of elbow points that need to be detected on the QF-PCR chemical reaction graph.
The parameters may include, but are not limited to parameters such as Fluorescence Intensity (FI) value of the one or more elbow points detected on the QF-PCR chemical reaction graph, a time of each of the elbow points, Fluorescence Intensity (FI) values at certain points of the QF-PCR chemical reaction graph, etc.
The extracted parameters are then used to classify the test assays, say through SVM or another supervised learning method, as known in the art.